blackPrior Comparisons. We show quantitative results compared with different priors in Tab. REF . And qualitative reuslts are illustrated in Fig. REF .
Note that the baseline model is also finetuned with the same steps on the subset. All priors can improve the performance based on the baselineblackWireframe lines do not work as an independent prior for image inpainting in this paper. Because lines are more effective in man-made scenes, while some cases have no lines at all. The effectiveness of line based on canny edge has been demonstrated in MST [1]}..
Both lines and edges after E-NMS can be upsampled by our SSU, while other priors suffer from artifacts in boundaries during the HR inpainting as in Tab. REF  and Fig. REF .
HOG and LR-RGB have discouraged improvements in Tab. REF  without proper structures.
Our conference version (Canny+Line) gets moderate performance compared with other priors, while L-Edges (HED, CATS, DexiNed) achieve better results in FID. Furthermore, CATS enjoys best performance among three L-Edge priors.
Moreover, semantic segmentation works well, but we have only reported the upper bound of semantic performance here due to the gap of segmentation results from corrupted images.
Gradients also greatly facilitate the inpainting performance, especially in PSNR and SSIM.
Though RTV is also competitive, but it still enjoys less priority compared with L-Edges and gradients. Besides, gradient and RTV fail to be generalized to HR with the SSU.
